Intrusion Detection based on Graph oriented Big Data Analytics

Intrusion detection has been the subject of numerous studies in industry and academia. Still, Cybersecurity analysts always want greater precision and global threat analysis to secure their systems in cyberspace. To improve the intrusion detection system, the visualization of the security events in...

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Bibliographic Details
Published inProcedia computer science Vol. 176; pp. 572 - 581
Main Authors Abid, Ahlem, Jemili, Farah
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2020
Subjects
Apache Spark
Big Data
Graph
Intrusion detection
k2 algorithm
Machine Learning
MAWILab
Microsoft Azure Cloud
Structured Streaming
MAWILab
Microsoft Azure Cloud
Graph
Intrusion detection
Big Data
Machine Learning
Structured Streaming
Apache Spark
k2 algorithm
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Summary:Intrusion detection has been the subject of numerous studies in industry and academia. Still, Cybersecurity analysts always want greater precision and global threat analysis to secure their systems in cyberspace. To improve the intrusion detection system, the visualization of the security events in the form of graphs and diagrams is significant to improve the accuracy of alerts. In this paper, we propose an approach of an IDS based on cloud computing, big data technique and, using a machine learning graph algorithm which can detect in real-time different attacks as early as possible. We use the MAWILab intrusion detection dataset. We choose Microsoft Azure as a unified cloud environment to load our dataset on Azure blob storage. We implement the k2 algorithm, which is a graphical machine learning algorithm to classify attacks. Our system showed a great performance due to the graphical machine learning algorithm and Apache Spark structured streaming engine.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2020.08.059